图像分类案例1

Kaggle上的图像分类（CIFAR-10)

现在，我们将运用在前面几节中学到的知识来参加Kaggle竞赛，该竞赛解决了CIFAR-10图像分类问题。比赛网址是https://www.kaggle.com/c/cifar-10

# 本节的网络需要较长的训练时间
# 可以在Kaggle访问：
# https://www.kaggle.com/boyuai/boyu-d2l-image-classification-cifar-10
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import os
import time

print("PyTorch Version: ",torch.__version__)

PyTorch Version:  1.3.0

获取和组织数据集

比赛数据分为训练集和测试集。训练集包含 50,000 图片。测试集包含 300,000 图片。两个数据集中的图像格式均为PNG，高度和宽度均为32像素，并具有三个颜色通道（RGB）。图像涵盖10个类别：飞机，汽车，鸟类，猫，鹿，狗，青蛙，马，船和卡车。 为了更容易上手，我们提供了上述数据集的小样本。“ train_tiny.zip”包含 80 训练样本，而“ test_tiny.zip”包含100个测试样本。它们的未压缩文件夹名称分别是“ train_tiny”和“ test_tiny”。

图像增强

data_transform = transforms.Compose([
    transforms.Resize(40),
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32),
    transforms.ToTensor()
])
trainset = torchvision.datasets.ImageFolder(root='/home/kesci/input/CIFAR102891/cifar-10/train'
                                            , transform=data_transform)

trainset[0][0].shape

torch.Size([3, 32, 32])

data = [d[0].data.cpu().numpy() for d in trainset]
np.mean(data)

0.4676536

np.std(data)

0.23926772

# 图像增强
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),  #先四周填充0，再把图像随机裁剪成32*32
    transforms.RandomHorizontalFlip(),  #图像一半的概率翻转，一半的概率不翻转
    transforms.ToTensor(),
    transforms.Normalize((0.4731, 0.4822, 0.4465), (0.2212, 0.1994, 0.2010)), #R,G,B每层的归一化用到的均值和方差
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4731, 0.4822, 0.4465), (0.2212, 0.1994, 0.2010)),
])

导入数据集

train_dir = '/home/kesci/input/CIFAR102891/cifar-10/train'
test_dir = '/home/kesci/input/CIFAR102891/cifar-10/test'

trainset = torchvision.datasets.ImageFolder(root=train_dir, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=256, shuffle=True)

testset = torchvision.datasets.ImageFolder(root=test_dir, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=256, shuffle=False)

classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'forg', 'horse', 'ship', 'truck']

定义模型

ResNet-18网络结构：ResNet全名Residual Network残差网络。Kaiming He 的《Deep Residual Learning for Image Recognition》获得了CVPR最佳论文。他提出的深度残差网络在2015年可以说是洗刷了图像方面的各大比赛，以绝对优势取得了多个比赛的冠军。而且它在保证网络精度的前提下，将网络的深度达到了152层，后来又进一步加到1000的深度。

class ResidualBlock(nn.Module):   # 我们定义网络时一般是继承的torch.nn.Module创建新的子类

    def __init__(self, inchannel, outchannel, stride=1):
        super(ResidualBlock, self).__init__()
        #torch.nn.Sequential是一个Sequential容器，模块将按照构造函数中传递的顺序添加到模块中。
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False), 
            # 添加第一个卷积层,调用了nn里面的Conv2d（）
            nn.BatchNorm2d(outchannel), # 进行数据的归一化处理
            nn.ReLU(inplace=True), # 修正线性单元，是一种人工神经网络中常用的激活函数
            nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(outchannel)
        )
        self.shortcut = nn.Sequential() 
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(outchannel)
            )
        #  便于之后的联合,要判断Y = self.left(X)的形状是否与X相同

    def forward(self, x): # 将两个模块的特征进行结合，并使用ReLU激活函数得到最终的特征。
        out = self.left(x)
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, ResidualBlock, num_classes=10):
        super(ResNet, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential( # 用3个3x3的卷积核代替7x7的卷积核，减少模型参数
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        ) 
        self.layer1 = self.make_layer(ResidualBlock, 64,  2, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
        self.fc = nn.Linear(512, num_classes)

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)   #第一个ResidualBlock的步幅由make_layer的函数参数stride指定
        # ，后续的num_blocks-1个ResidualBlock步幅是1
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out


def ResNet18():
    return ResNet(ResidualBlock)

训练和测试

# 定义是否使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 超参数设置
EPOCH = 20   #遍历数据集次数
pre_epoch = 0  # 定义已经遍历数据集的次数
LR = 0.1        #学习率

# 模型定义-ResNet
net = ResNet18().to(device)

# 定义损失函数和优化方式
criterion = nn.CrossEntropyLoss()  #损失函数为交叉熵，多用于多分类问题
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=5e-4) 
#优化方式为mini-batch momentum-SGD，并采用L2正则化（权重衰减）

# 训练
if __name__ == "__main__":
    print("Start Training, Resnet-18!")
    num_iters = 0
    for epoch in range(pre_epoch, EPOCH):
        print('\nEpoch: %d' % (epoch + 1))
        net.train()
        sum_loss = 0.0
        correct = 0.0
        total = 0
        for i, data in enumerate(trainloader, 0): 
            #用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列，同时列出数据和数据下标，
            #下标起始位置为0，返回 enumerate(枚举) 对象。
            
            num_iters += 1
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)
            optimizer.zero_grad()  # 清空梯度

            # forward + backward
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            sum_loss += loss.item() * labels.size(0)
            _, predicted = torch.max(outputs, 1) #选出每一列中最大的值作为预测结果
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
            # 每20个batch打印一次loss和准确率
            if (i + 1) % 20 == 0:
                print('[epoch:%d, iter:%d] Loss: %.03f | Acc: %.3f%% '
                        % (epoch + 1, num_iters, sum_loss / (i + 1), 100. * correct / total))

    print("Training Finished, TotalEPOCH=%d" % EPOCH)